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Distributed key-value stores (KVS) are a well-established approach for cloud data-intensive applications, but they were not designed to consider workloads with data access skew, mainly caused by popular data. In this work, we analyze the problem of replica placement on KVS for workloads with data access skew. We formally define our problem as a multi-objective optimization and present the PopRing approach based on genetic algorithm to find a new replica placement scheme. We also use OpenStack-Swift as the baseline to evaluate the performance improvements of PopRing under different configurations. A moderate PopRing configuration reduced in 52% the load imbalance and in 32% the replica placement maintenance while requiring the reconfiguration (data movement) of only 6% of total system data.